A Novel Machine Learning Approach to Working Memory Evaluation Using Resting-State EEG Data

Abstract

The human working memory and its cognitive functionality are essential for a range of fundamental to complex processes throughout our entire life. Damage and abnormalities can cause severe effects on an individual’s life. Predictive healthcare analytics can support and accelerate the diagnosis of such effects in the early stages. Using restingstate EEG data and the results of a cognitive testbattery for attentional performance, we developed a machine learning approach to evaluate the human working memory and detect hyperor hypoactivation. By predicting the testbattery results using the EEG recording of a patient, we enable a fast, objective, and accurate evaluation. Furthermore, we identified the most relevant brain regions (prefrontal cortex and dorsolateral prefrontal cortex) and the corresponding frequency subbands (9.511.5 Hz and 1113 Hz). With a balanced accuracy of 87.50%, our results set a new benchmark in evaluating the working memory using only restingstate EEG recordings